Advances in Computational Fluid Dynamics and Artificial Intelligence for the Water Sector

About this Research Topic

Background

Computational fluid dynamics (CFD) has increasingly attracted the attention of researchers as it enables the resolution of the full Navier–Stokes equations. These models have been applied to water systems to investigate issues related to the water–energy nexus, routine operation, and transient flows in monophasic (water) and biphasic (water–air) regimes, among others. A key challenge, however, lies in the considerable computational effort required. While CFD is well established and used across industry and agencies, barriers such as computational cost, model setup complexity, and cross-scale representation can still limit its broader, routine use in some planning and design contexts. In recent years, Artificial Intelligence (AI) has emerged as a complementary approach—not only to accelerate simulations, but also to enable knowledge discovery, joint learning with physics-based models, and improved handling of multi-scale phenomena.

This Research Topic aims to showcase how Computational Fluid Dynamics (CFD) can be effectively applied to the rational planning, design, and operation of water infrastructure through integration with Artificial Intelligence (AI) tools. While traditional CFD simulations remain essential, their outputs can be harnessed for the training, validation, and testing of machine learning models. Beyond reducing computational burden, this synergy supports hybrid physics–ML modelling, data assimilation, uncertainty quantification, and the development of robust digital twins. It also enables deeper insights into complex hydraulic phenomena (e.g., air–water interactions) and scale-bridging strategies, ultimately delivering practical, explainable solutions for engineers, utilities, and policymakers. The Research Topic seeks to highlight cutting-edge approaches that bridge high-fidelity modelling with data-driven intelligence, making advanced computational techniques more accessible, interpretable, and impactful in water engineering practice.

This Research Topic invites high-quality technical contributions that explore the application of Computational Fluid Dynamics (CFD), enhanced by Artificial Intelligence (AI) techniques, to advance practical use in the water sector. The overarching aim is to promote innovative approaches that reduce computational burden, enhance accuracy and interpretability, and facilitate integration of CFD into engineering practice for the planning, design, and operation of water systems. Submissions are encouraged on, but not limited to, the following topics:

• Application of CFD in water systems.
• Integration of AI techniques with CFD (e.g., surrogate and reduced-order models, physics-informed neural networks, hybrid solvers).
• Development of digital twins for water systems based on CFD and data assimilation.
• Strategies for computational efficiency (e.g., adaptive meshing, ROMs, multi-fidelity approaches, HPC–AI workflows).
• Analysis and scale-bridging of complex air–water interactions and multiphase flows.
• Training, validation, and testing frameworks that leverage CFD-generated data and real-world measurements.
• Uncertainty quantification, error estimation, and explainability in AI–CFD workflows.
• Knowledge discovery and model interpretability enabled by AI applied to CFD outputs.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Technology and Code

Keywords: Computational Fluid Dynamics, Machine Learning, Digitalisation, Fluid Mechanics, Digital Twin.

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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